Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "155" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 18 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 18 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459996 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.777215 | -0.814439 | 12.440497 | -0.724952 | 7.370893 | -0.064093 | 1.431287 | 0.443821 | 0.0418 | 0.6156 | 0.4642 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.264821 | -1.025920 | 9.926354 | -0.953263 | 7.855814 | 0.261827 | 1.595959 | 1.234874 | 0.0413 | 0.5987 | 0.4502 | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.185806 | -1.322054 | 9.766744 | -1.133625 | 9.237896 | 0.041540 | 1.241151 | 1.147454 | 0.0395 | 0.6040 | 0.4673 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.177211 | -1.054427 | 9.565783 | -1.240236 | 9.136933 | -0.379533 | 1.332020 | 1.910036 | 0.0435 | 0.6024 | 0.4655 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.638929 | -1.140035 | 8.507656 | -0.891892 | 8.104004 | -0.225074 | 1.260177 | 1.533779 | 0.0390 | 0.6019 | 0.4695 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.587007 | -1.219263 | 9.861286 | -1.435466 | 10.868273 | -0.308460 | 0.956364 | 2.354340 | 0.0391 | 0.6009 | 0.4575 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 99.57% | 0.00% | 0.00% | - | - | 9.806463 | -1.037759 | 9.576143 | -1.077304 | 6.430232 | -0.052513 | 2.281730 | 1.661760 | 0.0443 | 0.6103 | 0.4675 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.137831 | -1.021981 | 10.486533 | -1.335005 | 9.482703 | 0.154501 | 6.087507 | -0.809212 | 0.0414 | 0.6302 | 0.4597 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.197824 | -1.030999 | 9.722209 | -0.951794 | 7.303152 | 3.213728 | 2.706043 | 3.786307 | 0.0413 | 0.6102 | 0.4700 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 98.32% | 0.00% | 0.00% | - | - | 10.801976 | -1.224987 | 10.097112 | -1.411951 | 9.492571 | 11.634578 | 3.795294 | 2.562359 | 0.0499 | 0.6249 | 0.4711 | nan | nan |
| 2459983 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.502166 | -0.852020 | 9.644726 | -1.261934 | 9.335004 | -0.101045 | 3.708240 | -0.074340 | 0.0438 | 0.6537 | 0.4772 | nan | nan |
| 2459982 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 8.737307 | 0.292787 | 8.158094 | -0.882057 | 4.564281 | 0.158875 | 2.526077 | -0.371504 | 0.0424 | 0.6727 | 0.4655 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.571302 | -0.952272 | 10.263578 | -1.537443 | 10.506934 | -0.062257 | 1.366482 | 1.511270 | 0.0436 | 0.6113 | 0.4705 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.224020 | -0.688928 | 9.224856 | -1.244776 | 9.114194 | 0.361645 | 5.370107 | -0.668313 | 0.0439 | 0.6419 | 0.4652 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.660020 | -0.988508 | 8.534873 | -1.327349 | 9.013399 | 0.042096 | 1.357850 | 1.636462 | 0.0415 | 0.6060 | 0.4702 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.836365 | -1.030215 | 9.274664 | -1.429282 | 9.407683 | -0.338071 | 1.247834 | 1.713682 | 0.0380 | 0.6060 | 0.4705 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.106323 | -0.788193 | 9.122008 | -1.266298 | 9.274816 | 1.091825 | 1.404300 | 0.327845 | 0.0452 | 0.5686 | 0.4337 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.963726 | -0.895562 | 9.593633 | -1.408942 | 9.514711 | -0.097687 | 1.642067 | 0.882370 | 0.0396 | 0.6117 | 0.4672 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Power | 12.440497 | 10.777215 | -0.814439 | 12.440497 | -0.724952 | 7.370893 | -0.064093 | 1.431287 | 0.443821 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 10.264821 | 10.264821 | -1.025920 | 9.926354 | -0.953263 | 7.855814 | 0.261827 | 1.595959 | 1.234874 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 12.185806 | 12.185806 | -1.322054 | 9.766744 | -1.133625 | 9.237896 | 0.041540 | 1.241151 | 1.147454 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 10.177211 | -1.054427 | 10.177211 | -1.240236 | 9.565783 | -0.379533 | 9.136933 | 1.910036 | 1.332020 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 9.638929 | -1.140035 | 9.638929 | -0.891892 | 8.507656 | -0.225074 | 8.104004 | 1.533779 | 1.260177 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 11.587007 | -1.219263 | 11.587007 | -1.435466 | 9.861286 | -0.308460 | 10.868273 | 2.354340 | 0.956364 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 9.806463 | 9.806463 | -1.037759 | 9.576143 | -1.077304 | 6.430232 | -0.052513 | 2.281730 | 1.661760 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 12.137831 | -1.021981 | 12.137831 | -1.335005 | 10.486533 | 0.154501 | 9.482703 | -0.809212 | 6.087507 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 11.197824 | -1.030999 | 11.197824 | -0.951794 | 9.722209 | 3.213728 | 7.303152 | 3.786307 | 2.706043 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | nn Temporal Variability | 11.634578 | 10.801976 | -1.224987 | 10.097112 | -1.411951 | 9.492571 | 11.634578 | 3.795294 | 2.562359 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 10.502166 | 10.502166 | -0.852020 | 9.644726 | -1.261934 | 9.335004 | -0.101045 | 3.708240 | -0.074340 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 8.737307 | 8.737307 | 0.292787 | 8.158094 | -0.882057 | 4.564281 | 0.158875 | 2.526077 | -0.371504 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Temporal Variability | 10.506934 | -0.952272 | 9.571302 | -1.537443 | 10.263578 | -0.062257 | 10.506934 | 1.511270 | 1.366482 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Power | 9.224856 | -0.688928 | 9.224020 | -1.244776 | 9.224856 | 0.361645 | 9.114194 | -0.668313 | 5.370107 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 9.660020 | 9.660020 | -0.988508 | 8.534873 | -1.327349 | 9.013399 | 0.042096 | 1.357850 | 1.636462 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 9.836365 | -1.030215 | 9.836365 | -1.429282 | 9.274664 | -0.338071 | 9.407683 | 1.713682 | 1.247834 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 10.106323 | 10.106323 | -0.788193 | 9.122008 | -1.266298 | 9.274816 | 1.091825 | 1.404300 | 0.327845 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | RF_maintenance | ee Shape | 9.963726 | -0.895562 | 9.963726 | -1.408942 | 9.593633 | -0.097687 | 9.514711 | 0.882370 | 1.642067 |